In our experiments with pixel-based LBS, we use a "deconvolutional" network as the reconstruction model with an architecture that is the transpose of the posterior's encoder (see later Appendix sections for details). In the LBS loss, we use a value of \(\beta =1\) . We present results of the pixel-based LBS model in Figure REF , compared with the LBS feature-based model and the surprisal in pixel space baseline from [1]}.
